1 rANOMALY step-by-step use case.

1.1 Help

Each function have a detailed help accessible in R via ?{funtion}.

1.2 Tests datasets

The dataset can be downloaded via this link.

This tutorial assume that you have extracted all the read file in a folder named reads along with the sample-metadata.csv file.

We share a 24 samples test dataset extract from rats feces at two different time (t0 & t50) and in two nutrition conditions. Also included two extraction control sample (blank).

sm <- read.table("sample_metadata.csv", sep="\t",header=TRUE)
DT::datatable(sm)
load("decontam_out/robjects.Rdata")

1.3 ASV definition with DADA2

The first step will be the creation of ASVs (Amplicon Sequence Variants) thanks to the dada2 package. In rANOMALY, only one function is needed to compute all the different steps require from this package.

Sample names will be extracted from the file name, so files must be formatted as followed : {sample-id1}_R1.fastq.gz {sample-id1}_R2.fastq.gz etc…

dada_res = dada2_fun(path="./reads", dadapool = "pseudo", compress=TRUE, plot=FALSE)

Main output: - read_tracking.csv that summarize the read number after each filtering step.

DT::datatable(read.table("dada2_out/read_tracking.csv",sep="\t",header=TRUE))

The sample names extracted from the file name. We consider as sample name anything that is before the first underscore. This must match the sample names that are in sample metadata files. input: raw read number. filtered: after dada2 filtering step: no N’s in sequence, low quality, and phiX. denoisedF & denoisedR: after denoising. Forward & Reverse. merged: after merging R1 & R2. nonchim: after chimeras filtering.

  • dada2_robjects.Rdata with raw ASV table and representative sequences in objects otu.table, seqtab.export & seqtab.nochim.
  • raw_asv-table.csv
  • rep-seqs.fna

1.4 Taxonomic assignment

This function uses IDTAXA function from DECIPHER package, and allows to use 2 differents databases. It keeps the best assignation on 2 criteria, resolution (depth) and confidence. The final taxonomy is validated by multiple ancestors taxa and incongruity correction step.

We share the latest databases we use in the IDTAXA format in this link. You can also generate your own database following those instructions and scripts we provide in another repository.

tax.table = assign_taxo_fun(dada_res = dada_res, id_db = c("path_to_your_banks/silva/SILVA_SSU_r132_March2018.RData","path_to_your_banks/DAIRYdb_v1.2.0_20190222_IDTAXA.RData") )

Main output: - taxo_robjects.Rdata with taxonomy in phyloseq format in tax.table object. - final_tax_table.csv the final assignation table that will be use in next steps. - allDB_tax_table.csv raw assignations from the two databases, mainly for debugging.

1.5 Phylogenetic Tree

The phylogenetic tree from the representative sequences is generated using phangorn and DECIPHER packages.

tree = generate_tree_fun(dada_res)

Main output: - tree_robjects.Rdata with phylogenetic tree object in phyloseq format.

1.6 Phyloseq object

To create a phyloseq object, we need to merge four objects and one file: - the asv table otu.table and the representative sequences seqtab.nochim from dada2_robjects.Rdata - a taxonomy table taxo_robjects.Rdata from taxo_robjects.Rdata - the phylogenetic tree tree from tree_robjects.Rdata - metadata from sample-metadata.csv

data = generate_phyloseq_fun(dada_res = dada_res, taxtable = tax.table, tree = tree, metadata = "./sample_metadata.csv")

Main output: - robjects.Rdata with phyloseq object in data for raw counts and data_rel for relative abundance.

1.7 Decontamination

The decontam_fun function uses decontam R package with control samples to filter contaminants. The decontam package offers two main methods, frequency and prevalence (and then you can combine those methods). For frequency method, it is mandatory to have the dna concentration of each sample in phyloseq (and hence in the sample-metadata.csv). “In this method, the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.” In the prevalence methods no need of DNA quantification. “In this method, the prevalence (presence/absence across samples) of each sequence feature in true positive samples is compared to the prevalence in negative controls to identify contaminants.

Tips: sequencing plateforms often quantify the DNA before sequencing, but do not automaticaly give the information. Just ask for it ;).

Our function integrates the basics ASV frequency (nb_reads_ASV/nb_total_reads) and prevalence (nb_sample_ASV/nb_total_sample) filtering. As in our lab we had a known recurrent contaminant we included an option to filter out ASV based on they taxa names.

data = decontam_fun(data = data, domain = "Bacteria", column = "type", ctrl_identifier = "control", spl_identifier = "sample", number = 100)

Main output: - robjects.Rdata with contaminant filtered phyloseq object named data. - Exclu_out.csv list of filtered ASVs for each filtering step. - Kronas before and after filtering. - raw_asv-table.csv & relative_asv-table.csv. - venndiag_filtering.png.

venndiag

venndiag

1.8 Plots, diversity and statistics

!!! We are currently developping a ShinyApp to visualize your data, sub-select your samples/taxons and do all those analyses interactively !!! ExploreMetabar

1.8.1 Rarefaction curves

In order to observe the sampling depth of each samples we start by plotting rarefactions curves. Those plots are generated by Plotly which makes the plots interactive.

rarefaction(data, "souche_temps", 100 )
## rarefying sample SB1-Sauv0
## rarefying sample SB10-Mut0
## rarefying sample SB11-Mut0
## rarefying sample SB12-Mut0
## rarefying sample SB13-Sauv50
## rarefying sample SB14-Sauv50
## rarefying sample SB15-Sauv50
## rarefying sample SB16-Sauv50
## rarefying sample SB17-Sauv50
## rarefying sample SB18-Sauv50
## rarefying sample SB19-Mut50
## rarefying sample SB2-Sauv0
## rarefying sample SB20-Mut50
## rarefying sample SB21-Mut50
## rarefying sample SB22-Mut50
## rarefying sample SB23-Mut50
## rarefying sample SB24-Mut50
## rarefying sample SB3-Sauv0
## rarefying sample SB4-Sauv0
## rarefying sample SB5-Sauv0
## rarefying sample SB6-Sauv0
## rarefying sample SB7-Mut0
## rarefying sample SB8-Mut0
## rarefying sample SB9-Mut0
## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

1.8.2 Composition plots

Composition plots reveals here the top 10 genus present in our samples. #TODO Ord1 option control the… Fact1 option control the…

1.8.2.1 Relative abundance

bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = TRUE)

1.8.2.2 Raw abundance

bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = FALSE)

1.8.3 Diversity analyses

1.8.3.1 Alpha diversity

This function computes various alpha diversity indexes and returns

alpha <- diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
                    column3 = "", supcovs = "", measures = c("Observed", "Shannon") )
## INFO [2020-08-20 11:43:20] Alpha diversity tab ...
## INFO [2020-08-20 11:43:20] Done.
## INFO [2020-08-20 11:43:20] Plotting ...
## INFO [2020-08-20 11:43:20] Done.
## INFO [2020-08-20 11:43:21] ANOVA ...
## INFO [2020-08-20 11:43:21] Done.
## INFO [2020-08-20 11:43:21] Finish.
1.8.3.1.1 Tables
pander(alpha$alphatable, style='rmarkdown')
  Observed Shannon
SB1.Sauv0 41 1.477
SB10.Mut0 40 2.073
SB11.Mut0 51 2.178
SB12.Mut0 38 2.116
SB13.Sauv50 46 2.691
SB14.Sauv50 57 2.905
SB15.Sauv50 50 2.793
SB16.Sauv50 52 2.8
SB17.Sauv50 49 2.624
SB18.Sauv50 54 2.831
SB19.Mut50 66 2.638
SB2.Sauv0 26 2.099
SB20.Mut50 72 2.721
SB21.Mut50 79 3.062
SB22.Mut50 81 2.81
SB23.Mut50 84 3.175
SB24.Mut50 90 3.148
SB3.Sauv0 19 0.1962
SB4.Sauv0 41 2.52
SB5.Sauv0 46 1.923
SB6.Sauv0 46 1.067
SB7.Mut0 33 2.256
SB8.Mut0 58 2.089
SB9.Mut0 50 2.237

1.8.3.2 Boxplots

alpha$plot

1.8.3.3 ANOVA results

pander(alpha$anova)
Analysis of Variance Model #### Beta diversity
  Df Sum Sq Mean Sq F value Pr(>F)
Depth 1 1.777 1.777 10.1 0.004731
souche 1 0.52 0.52 2.955 0.101
temps 1 5.141 5.141 29.22 2.727e-05
Residuals 20 3.519 0.176 NA NA
beta <- diversity_beta_fun(data = data, output = "./plot_div_beta/", glom = "ASV", column1 = "temps", column2 = "souche", covar ="")
## INFO [2020-08-20 11:43:22] Option1...
## [1] "t0"  "t50"
## INFO [2020-08-20 11:43:22] Split table t0...
## INFO [2020-08-20 11:43:22] Done.
## [1] ""
## INFO [2020-08-20 11:43:22] No glom ...
## INFO [2020-08-20 11:43:22] Bray ...
## 
##  mutant sauvage 
##       6       6 
## INFO [2020-08-20 11:43:23] Done
## INFO [2020-08-20 11:43:23] Unifrac ...
## INFO [2020-08-20 11:43:23] Done
## INFO [2020-08-20 11:43:23] wunifrac ...
## INFO [2020-08-20 11:43:23] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.53973 0.53973  2.8355 0.17954 0.015984 * 
## souche     1   0.75338 0.75338  3.9580 0.25061 0.003996 **
## Residuals  9   1.71311 0.19035         0.56985            
## Total     11   3.00623                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  0.952842 4.640344 0.3169559   0.005      0.005   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.12045 0.120447  1.6362 0.12272 0.134865   
## souche     1   0.19850 0.198504  2.6965 0.20225 0.005994 **
## Residuals  9   0.66253 0.073615         0.67503            
## Total     11   0.98148                  1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.2429196 3.289082 0.2475026   0.003      0.003   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.51694 0.51694  5.3962 0.32059 0.000999 ***
## souche     1   0.23337 0.23337  2.4360 0.14472 0.068931 .  
## Residuals  9   0.86218 0.09580         0.53469             
## Total     11   1.61249                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.3815338 3.099498 0.236612   0.048      0.048   .
## INFO [2020-08-20 11:43:23] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383252 
## Run 1 stress 0.1383248 
## ... New best solution
## ... Procrustes: rmse 0.0007111882  max resid 0.001641181 
## ... Similar to previous best
## Run 2 stress 0.1383248 
## ... Procrustes: rmse 5.18931e-05  max resid 0.0001092668 
## ... Similar to previous best
## Run 3 stress 0.2136661 
## Run 4 stress 0.1415946 
## Run 5 stress 0.1383252 
## ... Procrustes: rmse 0.0003841452  max resid 0.0008622343 
## ... Similar to previous best
## Run 6 stress 0.1383256 
## ... Procrustes: rmse 0.0005469171  max resid 0.00126535 
## ... Similar to previous best
## Run 7 stress 0.221726 
## Run 8 stress 0.1415932 
## Run 9 stress 0.1415945 
## Run 10 stress 0.2116422 
## Run 11 stress 0.141596 
## Run 12 stress 0.1383263 
## ... Procrustes: rmse 0.0008088911  max resid 0.001955122 
## ... Similar to previous best
## Run 13 stress 0.138326 
## ... Procrustes: rmse 0.000676254  max resid 0.00159838 
## ... Similar to previous best
## Run 14 stress 0.1383279 
## ... Procrustes: rmse 0.0006174498  max resid 0.00120667 
## ... Similar to previous best
## Run 15 stress 0.1383254 
## ... Procrustes: rmse 0.0004564917  max resid 0.001059926 
## ... Similar to previous best
## Run 16 stress 0.1471255 
## Run 17 stress 0.1383255 
## ... Procrustes: rmse 0.0008526309  max resid 0.001598426 
## ... Similar to previous best
## Run 18 stress 0.138326 
## ... Procrustes: rmse 0.0007330918  max resid 0.001713313 
## ... Similar to previous best
## Run 19 stress 0.1383254 
## ... Procrustes: rmse 0.0004549941  max resid 0.001127637 
## ... Similar to previous best
## Run 20 stress 0.1415929 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383248 
## Run 1 stress 0.138326 
## ... Procrustes: rmse 0.00116427  max resid 0.002224766 
## ... Similar to previous best
## Run 2 stress 0.1383257 
## ... Procrustes: rmse 0.0008774465  max resid 0.001741337 
## ... Similar to previous best
## Run 3 stress 0.1383257 
## ... Procrustes: rmse 0.0007999315  max resid 0.001834499 
## ... Similar to previous best
## Run 4 stress 0.1416676 
## Run 5 stress 0.2172085 
## Run 6 stress 0.2172085 
## Run 7 stress 0.1383266 
## ... Procrustes: rmse 0.001330379  max resid 0.002410997 
## ... Similar to previous best
## Run 8 stress 0.141673 
## Run 9 stress 0.1383253 
## ... Procrustes: rmse 0.0006400523  max resid 0.001466764 
## ... Similar to previous best
## Run 10 stress 0.1416629 
## Run 11 stress 0.1416388 
## Run 12 stress 0.1415953 
## Run 13 stress 0.1416056 
## Run 14 stress 0.138325 
## ... Procrustes: rmse 0.0006067849  max resid 0.001377457 
## ... Similar to previous best
## Run 15 stress 0.1415934 
## Run 16 stress 0.1415938 
## Run 17 stress 0.1383253 
## ... Procrustes: rmse 0.0007010871  max resid 0.001357053 
## ... Similar to previous best
## Run 18 stress 0.1383299 
## ... Procrustes: rmse 0.002229527  max resid 0.004072661 
## ... Similar to previous best
## Run 19 stress 0.1383263 
## ... Procrustes: rmse 0.001282458  max resid 0.002347546 
## ... Similar to previous best
## Run 20 stress 0.1415945 
## *** Solution reached
## Run 0 stress 0.1396049 
## Run 1 stress 0.1554503 
## Run 2 stress 0.1396049 
## ... Procrustes: rmse 5.171482e-05  max resid 0.0001105483 
## ... Similar to previous best
## Run 3 stress 0.1396049 
## ... Procrustes: rmse 3.297584e-06  max resid 8.219886e-06 
## ... Similar to previous best
## Run 4 stress 0.2416939 
## Run 5 stress 0.2417539 
## Run 6 stress 0.139605 
## ... Procrustes: rmse 0.000201458  max resid 0.0004305822 
## ... Similar to previous best
## Run 7 stress 0.1396049 
## ... Procrustes: rmse 5.829218e-05  max resid 0.0001225176 
## ... Similar to previous best
## Run 8 stress 0.1554507 
## Run 9 stress 0.1396049 
## ... Procrustes: rmse 0.0001164561  max resid 0.000248645 
## ... Similar to previous best
## Run 10 stress 0.1396049 
## ... Procrustes: rmse 5.608239e-05  max resid 0.0001195182 
## ... Similar to previous best
## Run 11 stress 0.1396049 
## ... New best solution
## ... Procrustes: rmse 2.895523e-05  max resid 6.0345e-05 
## ... Similar to previous best
## Run 12 stress 0.1554503 
## Run 13 stress 0.139605 
## ... Procrustes: rmse 0.0001537489  max resid 0.0003312175 
## ... Similar to previous best
## Run 14 stress 0.1396049 
## ... Procrustes: rmse 3.796088e-05  max resid 6.718204e-05 
## ... Similar to previous best
## Run 15 stress 0.1396049 
## ... Procrustes: rmse 5.928586e-05  max resid 0.0001253705 
## ... Similar to previous best
## Run 16 stress 0.2524046 
## Run 17 stress 0.1396049 
## ... New best solution
## ... Procrustes: rmse 1.214634e-05  max resid 2.424074e-05 
## ... Similar to previous best
## Run 18 stress 0.1396049 
## ... Procrustes: rmse 5.139167e-05  max resid 0.000108197 
## ... Similar to previous best
## Run 19 stress 0.3075082 
## Run 20 stress 0.2748174 
## *** Solution reached
## Run 0 stress 0.04595665 
## Run 1 stress 0.08158394 
## Run 2 stress 0.08294192 
## Run 3 stress 0.0837327 
## Run 4 stress 0.05319186 
## Run 5 stress 0.08157902 
## Run 6 stress 0.05106463 
## Run 7 stress 0.05318663 
## Run 8 stress 0.04595632 
## ... New best solution
## ... Procrustes: rmse 9.027851e-05  max resid 0.000221114 
## ... Similar to previous best
## Run 9 stress 0.05317947 
## Run 10 stress 0.3236485 
## Run 11 stress 0.04595618 
## ... New best solution
## ... Procrustes: rmse 0.0009933373  max resid 0.002432997 
## ... Similar to previous best
## Run 12 stress 0.05106899 
## Run 13 stress 0.08294091 
## Run 14 stress 0.08157455 
## Run 15 stress 0.05317786 
## Run 16 stress 0.04595625 
## ... Procrustes: rmse 2.226313e-05  max resid 4.693294e-05 
## ... Similar to previous best
## Run 17 stress 0.08256781 
## Run 18 stress 0.08294084 
## Run 19 stress 0.08256771 
## Run 20 stress 0.04595598 
## ... New best solution
## ... Procrustes: rmse 7.318793e-05  max resid 0.00017893 
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 11:43:24] Done.
## INFO [2020-08-20 11:43:24] Saving ...
## INFO [2020-08-20 11:43:26] Supplement Beta plots ...
## INFO [2020-08-20 11:43:26] Done.
## INFO [2020-08-20 11:43:26] Split table t50...
## INFO [2020-08-20 11:43:26] Done.
## [1] ""
## INFO [2020-08-20 11:43:26] No glom ...
## INFO [2020-08-20 11:43:26] Bray ...
## 
##  mutant sauvage 
##       6       6 
## INFO [2020-08-20 11:43:26] Done
## INFO [2020-08-20 11:43:26] Unifrac ...
## INFO [2020-08-20 11:43:26] Done
## INFO [2020-08-20 11:43:26] wunifrac ...
## INFO [2020-08-20 11:43:26] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.06369 0.06369   3.118 0.03093 0.106893    
## souche     1   1.81185 1.81185  88.707 0.87981 0.000999 ***
## Residuals  9   0.18383 0.02043         0.08926             
## Total     11   2.05937                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  1.817719 75.21929 0.8826557   0.005      0.005   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.08744 0.08744   6.044 0.08564 0.010989 *  
## souche     1   0.80336 0.80336  55.529 0.78683 0.000999 ***
## Residuals  9   0.13021 0.01447         0.12753             
## Total     11   1.02100                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.8054894 37.37546 0.7889203   0.004      0.004   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2   Pr(>F)    
## Depth      1  0.005527 0.005527   2.215 0.02294 0.146853    
## souche     1  0.212963 0.212963  85.338 0.88385 0.000999 ***
## Residuals  9  0.022460 0.002496         0.09321             
## Total     11  0.240949                  1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.2133365 77.26041 0.8854005   0.001      0.001  **
## INFO [2020-08-20 11:43:26] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 7.297422e-05 
## Run 1 stress 9.072339e-05 
## ... Procrustes: rmse 7.768677e-05  max resid 0.0002166727 
## ... Similar to previous best
## Run 2 stress 0.2334764 
## Run 3 stress 9.005844e-05 
## ... Procrustes: rmse 9.739143e-05  max resid 0.0001717861 
## ... Similar to previous best
## Run 4 stress 9.95859e-05 
## ... Procrustes: rmse 9.322439e-05  max resid 0.0002091992 
## ... Similar to previous best
## Run 5 stress 8.897427e-05 
## ... Procrustes: rmse 0.0002216845  max resid 0.0005575271 
## ... Similar to previous best
## Run 6 stress 9.832949e-05 
## ... Procrustes: rmse 0.0001241593  max resid 0.0002434063 
## ... Similar to previous best
## Run 7 stress 9.452182e-05 
## ... Procrustes: rmse 0.0002364735  max resid 0.0005866198 
## ... Similar to previous best
## Run 8 stress 9.791438e-05 
## ... Procrustes: rmse 0.000263323  max resid 0.0006040692 
## ... Similar to previous best
## Run 9 stress 9.296734e-05 
## ... Procrustes: rmse 0.0002500079  max resid 0.0005780813 
## ... Similar to previous best
## Run 10 stress 8.761903e-05 
## ... Procrustes: rmse 0.0002291521  max resid 0.0005350754 
## ... Similar to previous best
## Run 11 stress 9.774804e-05 
## ... Procrustes: rmse 0.0002406461  max resid 0.0005989343 
## ... Similar to previous best
## Run 12 stress 8.903352e-05 
## ... Procrustes: rmse 7.276306e-05  max resid 0.0001593281 
## ... Similar to previous best
## Run 13 stress 8.927059e-05 
## ... Procrustes: rmse 6.852359e-05  max resid 0.0001890088 
## ... Similar to previous best
## Run 14 stress 8.472066e-05 
## ... Procrustes: rmse 8.880295e-05  max resid 0.0001598673 
## ... Similar to previous best
## Run 15 stress 9.494667e-05 
## ... Procrustes: rmse 0.0002560394  max resid 0.0005910742 
## ... Similar to previous best
## Run 16 stress 9.92707e-05 
## ... Procrustes: rmse 0.0002447724  max resid 0.000607732 
## ... Similar to previous best
## Run 17 stress 9.921249e-05 
## ... Procrustes: rmse 0.0001916681  max resid 0.0004861638 
## ... Similar to previous best
## Run 18 stress 8.928482e-05 
## ... Procrustes: rmse 8.723281e-05  max resid 0.0001592515 
## ... Similar to previous best
## Run 19 stress 7.930669e-05 
## ... Procrustes: rmse 8.405353e-05  max resid 0.0001571251 
## ... Similar to previous best
## Run 20 stress 9.108848e-05 
## ... Procrustes: rmse 0.0001178203  max resid 0.0002635204 
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 8.694326e-05 
## Run 1 stress 9.748641e-05 
## ... Procrustes: rmse 6.765283e-05  max resid 0.0001697715 
## ... Similar to previous best
## Run 2 stress 9.581008e-05 
## ... Procrustes: rmse 0.0002451187  max resid 0.0006773339 
## ... Similar to previous best
## Run 3 stress 9.023233e-05 
## ... Procrustes: rmse 2.273242e-05  max resid 4.192331e-05 
## ... Similar to previous best
## Run 4 stress 9.042644e-05 
## ... Procrustes: rmse 0.0002239383  max resid 0.0006292496 
## ... Similar to previous best
## Run 5 stress 9.397036e-05 
## ... Procrustes: rmse 0.0001409961  max resid 0.0002687657 
## ... Similar to previous best
## Run 6 stress 0.2890372 
## Run 7 stress 9.120384e-05 
## ... Procrustes: rmse 6.520305e-05  max resid 0.0001498366 
## ... Similar to previous best
## Run 8 stress 9.716996e-05 
## ... Procrustes: rmse 0.0002476234  max resid 0.000686722 
## ... Similar to previous best
## Run 9 stress 9.643596e-05 
## ... Procrustes: rmse 0.0002569899  max resid 0.0006643846 
## ... Similar to previous best
## Run 10 stress 9.886299e-05 
## ... Procrustes: rmse 0.0002509961  max resid 0.0006949432 
## ... Similar to previous best
## Run 11 stress 9.977757e-05 
## ... Procrustes: rmse 9.713435e-05  max resid 0.00021338 
## ... Similar to previous best
## Run 12 stress 8.285079e-05 
## ... New best solution
## ... Procrustes: rmse 7.368284e-05  max resid 0.0001614828 
## ... Similar to previous best
## Run 13 stress 9.266872e-05 
## ... Procrustes: rmse 6.92998e-05  max resid 0.0001830171 
## ... Similar to previous best
## Run 14 stress 7.135878e-05 
## ... New best solution
## ... Procrustes: rmse 6.453509e-05  max resid 0.0001604199 
## ... Similar to previous best
## Run 15 stress 9.265212e-05 
## ... Procrustes: rmse 0.0001433101  max resid 0.0002567619 
## ... Similar to previous best
## Run 16 stress 9.894863e-05 
## ... Procrustes: rmse 0.000258855  max resid 0.0006401064 
## ... Similar to previous best
## Run 17 stress 0.3140362 
## Run 18 stress 8.824241e-05 
## ... Procrustes: rmse 6.411572e-05  max resid 0.0001786811 
## ... Similar to previous best
## Run 19 stress 8.797687e-05 
## ... Procrustes: rmse 0.0001144256  max resid 0.0002361331 
## ... Similar to previous best
## Run 20 stress 9.520276e-05 
## ... Procrustes: rmse 0.0001340272  max resid 0.0002118414 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 9.441127e-05 
## Run 1 stress 9.163055e-05 
## ... New best solution
## ... Procrustes: rmse 7.530837e-05  max resid 0.0001744852 
## ... Similar to previous best
## Run 2 stress 9.212023e-05 
## ... Procrustes: rmse 9.70487e-05  max resid 0.0001909365 
## ... Similar to previous best
## Run 3 stress 8.361978e-05 
## ... New best solution
## ... Procrustes: rmse 7.343488e-05  max resid 0.0001720889 
## ... Similar to previous best
## Run 4 stress 9.177707e-05 
## ... Procrustes: rmse 6.425788e-05  max resid 0.0001863483 
## ... Similar to previous best
## Run 5 stress 9.938801e-05 
## ... Procrustes: rmse 6.958948e-05  max resid 0.0001991097 
## ... Similar to previous best
## Run 6 stress 9.465171e-05 
## ... Procrustes: rmse 0.000120524  max resid 0.0002486309 
## ... Similar to previous best
## Run 7 stress 9.643951e-05 
## ... Procrustes: rmse 0.0001610937  max resid 0.0003068382 
## ... Similar to previous best
## Run 8 stress 9.401982e-05 
## ... Procrustes: rmse 0.0001171673  max resid 0.0002542309 
## ... Similar to previous best
## Run 9 stress 9.508155e-05 
## ... Procrustes: rmse 7.59953e-05  max resid 0.0001615181 
## ... Similar to previous best
## Run 10 stress 9.846444e-05 
## ... Procrustes: rmse 0.0001158061  max resid 0.0002788113 
## ... Similar to previous best
## Run 11 stress 9.668601e-05 
## ... Procrustes: rmse 0.0001885189  max resid 0.0003120034 
## ... Similar to previous best
## Run 12 stress 9.581862e-05 
## ... Procrustes: rmse 6.824297e-05  max resid 0.0001954106 
## ... Similar to previous best
## Run 13 stress 8.994205e-05 
## ... Procrustes: rmse 5.67046e-05  max resid 0.0001377605 
## ... Similar to previous best
## Run 14 stress 9.748541e-05 
## ... Procrustes: rmse 8.222535e-05  max resid 0.000145379 
## ... Similar to previous best
## Run 15 stress 9.440691e-05 
## ... Procrustes: rmse 2.80087e-05  max resid 6.010457e-05 
## ... Similar to previous best
## Run 16 stress 9.964305e-05 
## ... Procrustes: rmse 3.756918e-05  max resid 8.724602e-05 
## ... Similar to previous best
## Run 17 stress 9.597292e-05 
## ... Procrustes: rmse 7.730236e-05  max resid 0.0001433209 
## ... Similar to previous best
## Run 18 stress 7.391807e-05 
## ... New best solution
## ... Procrustes: rmse 0.0001349231  max resid 0.000338976 
## ... Similar to previous best
## Run 19 stress 9.032969e-05 
## ... Procrustes: rmse 0.0001761176  max resid 0.0003438619 
## ... Similar to previous best
## Run 20 stress 9.955021e-05 
## ... Procrustes: rmse 0.0001569559  max resid 0.0004393414 
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.00169726 
## Run 1 stress 0.0002197232 
## ... New best solution
## ... Procrustes: rmse 0.009379533  max resid 0.01793379 
## Run 2 stress 0.00123796 
## Run 3 stress 0.002620137 
## Run 4 stress 9.97288e-05 
## ... New best solution
## ... Procrustes: rmse 0.0008186464  max resid 0.001464357 
## ... Similar to previous best
## Run 5 stress 9.683841e-05 
## ... New best solution
## ... Procrustes: rmse 6.559068e-05  max resid 0.0001697254 
## ... Similar to previous best
## Run 6 stress 0.002604824 
## Run 7 stress 0.002923216 
## Run 8 stress 0.001933772 
## Run 9 stress 0.0004806639 
## ... Procrustes: rmse 0.002793245  max resid 0.005198637 
## ... Similar to previous best
## Run 10 stress 0.001368991 
## Run 11 stress 9.513837e-05 
## ... New best solution
## ... Procrustes: rmse 0.0001475611  max resid 0.0003304377 
## ... Similar to previous best
## Run 12 stress 0.002448876 
## Run 13 stress 0.001702162 
## Run 14 stress 0.00172639 
## Run 15 stress 0.001758978 
## Run 16 stress 0.002145436 
## Run 17 stress 9.987843e-05 
## ... Procrustes: rmse 0.0002861659  max resid 0.0005287277 
## ... Similar to previous best
## Run 18 stress 0.001707582 
## Run 19 stress 0.001591586 
## Run 20 stress 0.0008198339 
## *** Solution reached
## INFO [2020-08-20 11:43:27] Done.
## INFO [2020-08-20 11:43:27] Saving ...

## INFO [2020-08-20 11:43:29] Supplement Beta plots ...
## INFO [2020-08-20 11:43:29] Done.
## INFO [2020-08-20 11:43:29] Global1...
## [1] ""
## INFO [2020-08-20 11:43:29] No glom ...
## INFO [2020-08-20 11:43:29] Bray ...
##      souche
## temps mutant sauvage
##   t0       6       6
##   t50      6       6
## INFO [2020-08-20 11:43:29] Done
## INFO [2020-08-20 11:43:29] Unifrac ...
## INFO [2020-08-20 11:43:30] Done
## INFO [2020-08-20 11:43:30] wunifrac ...
## INFO [2020-08-20 11:43:30] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  3.1218 0.06845 0.016983 *  
## temps      1    2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche     1    1.4711 1.47112  9.0493 0.19841 0.000999 ***
## Residuals 20    3.2514 0.16257         0.43851             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1  0.952842  4.640344 0.3169559   0.003     0.0048
## 2 t0-sauvage vs t50-sauvage  1  2.020676 28.967360 0.7433750   0.004     0.0048
## 3  t0-sauvage vs t50-mutant  1  2.197269 26.004113 0.7222540   0.003     0.0048
## 4  t0-mutant vs t50-sauvage  1  1.680832 11.591365 0.5368519   0.002     0.0048
## 5   t0-mutant vs t50-mutant  1  1.569713  9.826226 0.4956176   0.007     0.0070
## 6 t50-sauvage vs t50-mutant  1  1.817719 75.219295 0.8826557   0.004     0.0048
##   sig
## 1   *
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.13746 0.13746  2.4027 0.04730 0.050949 .  
## temps      1   0.99444 0.99444 17.3818 0.34220 0.000999 ***
## souche     1   0.62986 0.62986 11.0092 0.21674 0.000999 ***
## Residuals 20   1.14423 0.05721         0.39375             
## Total     23   2.90599                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1 0.2240538  3.250757 0.2453261   0.004     0.0048
## 2 t0-sauvage vs t50-sauvage  1 0.5057533 12.113203 0.5477815   0.003     0.0045
## 3  t0-sauvage vs t50-mutant  1 1.0565450 23.160697 0.6984382   0.002     0.0045
## 4  t0-mutant vs t50-sauvage  1 0.6002740 13.641788 0.5770201   0.003     0.0045
## 5   t0-mutant vs t50-mutant  1 0.8527236 17.813923 0.6404678   0.003     0.0045
## 6 t50-sauvage vs t50-mutant  1 0.7802130 37.696858 0.7903426   0.005     0.0050
##   sig
## 1   *
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.27641 0.27641  6.4387 0.14496 0.002997 ** 
## temps      1   0.44988 0.44988 10.4795 0.23593 0.000999 ***
## souche     1   0.32197 0.32197  7.4999 0.16885 0.000999 ***
## Residuals 20   0.85859 0.04293         0.45027             
## Total     23   1.90684                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##                       pairs Df SumsOfSqs   F.Model        R2 p.value p.adjusted
## 1   t0-sauvage vs t0-mutant  1 0.2067852  2.841008 0.2212450   0.050     0.0500
## 2 t0-sauvage vs t50-sauvage  1 0.4645524 13.418868 0.5729939   0.005     0.0072
## 3  t0-sauvage vs t50-mutant  1 0.5903252 15.585623 0.6091555   0.006     0.0072
## 4  t0-mutant vs t50-sauvage  1 0.2788751  6.969745 0.4107160   0.003     0.0072
## 5   t0-mutant vs t50-mutant  1 0.3237289  7.481746 0.4279748   0.004     0.0072
## 6 t50-sauvage vs t50-mutant  1 0.3916400 76.752213 0.8847292   0.003     0.0072
##   sig
## 1   .
## 2   *
## 3   *
## 4   *
## 5   *
## 6   *
## INFO [2020-08-20 11:43:30] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1278068 
## Run 2 stress 0.1004882 
## ... Procrustes: rmse 0.005823643  max resid 0.02234724 
## Run 3 stress 0.1004764 
## ... Procrustes: rmse 3.821424e-05  max resid 6.889213e-05 
## ... Similar to previous best
## Run 4 stress 0.1004882 
## ... Procrustes: rmse 0.005819609  max resid 0.02232734 
## Run 5 stress 0.1004883 
## ... Procrustes: rmse 0.00579385  max resid 0.02226849 
## Run 6 stress 0.1278184 
## Run 7 stress 0.1282064 
## Run 8 stress 0.1004764 
## ... Procrustes: rmse 1.237883e-05  max resid 3.540612e-05 
## ... Similar to previous best
## Run 9 stress 0.1004764 
## ... Procrustes: rmse 3.985522e-05  max resid 0.0001033497 
## ... Similar to previous best
## Run 10 stress 0.1004882 
## ... Procrustes: rmse 0.005829569  max resid 0.02236233 
## Run 11 stress 0.1004882 
## ... Procrustes: rmse 0.005809934  max resid 0.02229716 
## Run 12 stress 0.1004764 
## ... Procrustes: rmse 2.204869e-05  max resid 5.689465e-05 
## ... Similar to previous best
## Run 13 stress 0.1004764 
## ... Procrustes: rmse 2.012674e-05  max resid 7.370409e-05 
## ... Similar to previous best
## Run 14 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 7.810815e-06  max resid 2.141758e-05 
## ... Similar to previous best
## Run 15 stress 0.1004764 
## ... Procrustes: rmse 1.299291e-05  max resid 4.917161e-05 
## ... Similar to previous best
## Run 16 stress 0.1004764 
## ... Procrustes: rmse 6.421255e-05  max resid 0.0001871853 
## ... Similar to previous best
## Run 17 stress 0.1322902 
## Run 18 stress 0.1004882 
## ... Procrustes: rmse 0.005807036  max resid 0.0222636 
## Run 19 stress 0.1004764 
## ... Procrustes: rmse 2.01287e-05  max resid 5.353975e-05 
## ... Similar to previous best
## Run 20 stress 0.1004764 
## ... Procrustes: rmse 2.389933e-05  max resid 6.118326e-05 
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.127583 
## Run 2 stress 0.1275004 
## Run 3 stress 0.1004882 
## ... Procrustes: rmse 1.677829e-05  max resid 2.864545e-05 
## ... Similar to previous best
## Run 4 stress 0.1004882 
## ... Procrustes: rmse 2.331472e-05  max resid 5.632918e-05 
## ... Similar to previous best
## Run 5 stress 0.1004883 
## ... Procrustes: rmse 7.361098e-05  max resid 0.0002438938 
## ... Similar to previous best
## Run 6 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 0.005815704  max resid 0.0223052 
## Run 7 stress 0.1004766 
## ... Procrustes: rmse 9.99347e-05  max resid 0.000263001 
## ... Similar to previous best
## Run 8 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 1.145559e-05  max resid 2.963108e-05 
## ... Similar to previous best
## Run 9 stress 0.1278117 
## Run 10 stress 0.1004764 
## ... Procrustes: rmse 2.991178e-05  max resid 9.119642e-05 
## ... Similar to previous best
## Run 11 stress 0.1282049 
## Run 12 stress 0.1004764 
## ... Procrustes: rmse 1.148337e-05  max resid 3.044424e-05 
## ... Similar to previous best
## Run 13 stress 0.1004765 
## ... Procrustes: rmse 8.413438e-05  max resid 0.0002228466 
## ... Similar to previous best
## Run 14 stress 0.1278136 
## Run 15 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 1.082832e-05  max resid 2.927359e-05 
## ... Similar to previous best
## Run 16 stress 0.1278013 
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.00581578  max resid 0.02231153 
## Run 18 stress 0.1004768 
## ... Procrustes: rmse 7.019181e-05  max resid 0.0002005493 
## ... Similar to previous best
## Run 19 stress 0.133214 
## Run 20 stress 0.1278085 
## *** Solution reached
## Run 0 stress 0.1227056 
## Run 1 stress 0.1225313 
## ... New best solution
## ... Procrustes: rmse 0.01280186  max resid 0.04737191 
## Run 2 stress 0.1230686 
## Run 3 stress 0.1298941 
## Run 4 stress 0.1487401 
## Run 5 stress 0.1295963 
## Run 6 stress 0.1225312 
## ... New best solution
## ... Procrustes: rmse 0.000118533  max resid 0.0004953098 
## ... Similar to previous best
## Run 7 stress 0.1471614 
## Run 8 stress 0.124186 
## Run 9 stress 0.1487399 
## Run 10 stress 0.1298942 
## Run 11 stress 0.1227056 
## ... Procrustes: rmse 0.01280135  max resid 0.04743095 
## Run 12 stress 0.1227056 
## ... Procrustes: rmse 0.01280135  max resid 0.04743425 
## Run 13 stress 0.1246089 
## Run 14 stress 0.1484704 
## Run 15 stress 0.1475442 
## Run 16 stress 0.1298612 
## Run 17 stress 0.1225318 
## ... Procrustes: rmse 0.0002912653  max resid 0.001263143 
## ... Similar to previous best
## Run 18 stress 0.1686138 
## Run 19 stress 0.148097 
## Run 20 stress 0.1227056 
## ... Procrustes: rmse 0.01280147  max resid 0.04743224 
## *** Solution reached
## Run 0 stress 0.07629315 
## Run 1 stress 0.08609846 
## Run 2 stress 0.08609888 
## Run 3 stress 0.07677108 
## ... Procrustes: rmse 0.005849335  max resid 0.02149729 
## Run 4 stress 0.1007069 
## Run 5 stress 0.09543231 
## Run 6 stress 0.09543309 
## Run 7 stress 0.07741039 
## Run 8 stress 0.2063802 
## Run 9 stress 0.1027422 
## Run 10 stress 0.07697398 
## Run 11 stress 0.08609786 
## Run 12 stress 0.07676879 
## ... Procrustes: rmse 0.005578383  max resid 0.02054469 
## Run 13 stress 0.08158911 
## Run 14 stress 0.08134273 
## Run 15 stress 0.07676945 
## ... Procrustes: rmse 0.00575354  max resid 0.02128685 
## Run 16 stress 0.3811938 
## Run 17 stress 0.0954317 
## Run 18 stress 0.09983933 
## Run 19 stress 0.07740946 
## Run 20 stress 0.07740898 
## *** No convergence -- monoMDS stopping criteria:
##     20: stress ratio > sratmax
## INFO [2020-08-20 11:43:31] Done.
## INFO [2020-08-20 11:43:31] Saving ...

## INFO [2020-08-20 11:43:33] Supplement Beta plots ...
## INFO [2020-08-20 11:43:33] Done.
## INFO [2020-08-20 11:43:33] Global2...
## [1] ""
## INFO [2020-08-20 11:43:33] No glom ...
## INFO [2020-08-20 11:43:33] Bray ...
## 
##  t0 t50 
##  12  12 
## INFO [2020-08-20 11:43:33] Done
## INFO [2020-08-20 11:43:33] Unifrac ...
## INFO [2020-08-20 11:43:34] Done
## INFO [2020-08-20 11:43:34] wunifrac ...
## INFO [2020-08-20 11:43:34] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  2.2568 0.06845 0.056943 .  
## temps      1    2.1846 2.18458  9.7144 0.29463 0.000999 ***
## Residuals 21    4.7225 0.22488         0.63692             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##       pairs Df SumsOfSqs  F.Model       R2 p.value p.adjusted sig
## 1 t0 vs t50  1  2.348965 10.20159 0.316804   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.1408  0.1408  1.6722 0.04814 0.123876    
## temps      1    1.0156  1.0156 12.0618 0.34726 0.000999 ***
## Residuals 21    1.7682  0.0842         0.60460             
## Total     23    2.9246                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##       pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 t0 vs t50  1  1.024072 11.85414 0.3501533   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs  MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.14305 0.143045  4.7521 0.14129 0.002997 ** 
## temps      1   0.23722 0.237221  7.8807 0.23432 0.000999 ***
## Residuals 21   0.63213 0.030102         0.62439             
## Total     23   1.01240                  1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##       pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 t0 vs t50  1 0.2780463 8.329805 0.2746409   0.001      0.001  **
## INFO [2020-08-20 11:43:34] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1004884 
## ... Procrustes: rmse 0.005778965  max resid 0.02225563 
## Run 2 stress 0.1337333 
## Run 3 stress 0.1282048 
## Run 4 stress 0.1004882 
## ... Procrustes: rmse 0.005835325  max resid 0.02240664 
## Run 5 stress 0.1004764 
## ... Procrustes: rmse 8.015276e-06  max resid 2.282927e-05 
## ... Similar to previous best
## Run 6 stress 0.1278161 
## Run 7 stress 0.1004764 
## ... Procrustes: rmse 7.186298e-06  max resid 2.389786e-05 
## ... Similar to previous best
## Run 8 stress 0.1004765 
## ... Procrustes: rmse 3.511e-05  max resid 0.0001371272 
## ... Similar to previous best
## Run 9 stress 0.1004883 
## ... Procrustes: rmse 0.005842995  max resid 0.02239032 
## Run 10 stress 0.1004764 
## ... Procrustes: rmse 8.769913e-06  max resid 2.612497e-05 
## ... Similar to previous best
## Run 11 stress 0.1282048 
## Run 12 stress 0.1316111 
## Run 13 stress 0.1282054 
## Run 14 stress 0.1337266 
## Run 15 stress 0.1282047 
## Run 16 stress 0.1282046 
## Run 17 stress 0.1282059 
## Run 18 stress 0.1004764 
## ... Procrustes: rmse 1.1402e-05  max resid 3.68951e-05 
## ... Similar to previous best
## Run 19 stress 0.1278101 
## Run 20 stress 0.1278163 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.1282046 
## Run 2 stress 0.1004765 
## ... New best solution
## ... Procrustes: rmse 0.005817589  max resid 0.02227882 
## Run 3 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 2.382594e-05  max resid 4.854205e-05 
## ... Similar to previous best
## Run 4 stress 0.1004882 
## ... Procrustes: rmse 0.005816155  max resid 0.02230668 
## Run 5 stress 0.1004882 
## ... Procrustes: rmse 0.005812883  max resid 0.02228353 
## Run 6 stress 0.1278125 
## Run 7 stress 0.128206 
## Run 8 stress 0.1278106 
## Run 9 stress 0.1004882 
## ... Procrustes: rmse 0.005816782  max resid 0.0223074 
## Run 10 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 4.756458e-05  max resid 0.0001282969 
## ... Similar to previous best
## Run 11 stress 0.1282051 
## Run 12 stress 0.1004764 
## ... Procrustes: rmse 2.937284e-05  max resid 6.478681e-05 
## ... Similar to previous best
## Run 13 stress 0.1282046 
## Run 14 stress 0.1004882 
## ... Procrustes: rmse 0.005822232  max resid 0.0223718 
## Run 15 stress 0.1004882 
## ... Procrustes: rmse 0.00581379  max resid 0.02232067 
## Run 16 stress 0.1004764 
## ... Procrustes: rmse 1.849408e-05  max resid 5.629347e-05 
## ... Similar to previous best
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.005813968  max resid 0.02232192 
## Run 18 stress 0.1322855 
## Run 19 stress 0.1004882 
## ... Procrustes: rmse 0.005806415  max resid 0.02230706 
## Run 20 stress 0.1275823 
## *** Solution reached
## Run 0 stress 0.1211578 
## Run 1 stress 0.1211578 
## ... New best solution
## ... Procrustes: rmse 1.556443e-06  max resid 4.027713e-06 
## ... Similar to previous best
## Run 2 stress 0.1223139 
## Run 3 stress 0.1223137 
## Run 4 stress 0.1515542 
## Run 5 stress 0.1223129 
## Run 6 stress 0.1208049 
## ... New best solution
## ... Procrustes: rmse 0.01452796  max resid 0.05164647 
## Run 7 stress 0.1211578 
## ... Procrustes: rmse 0.01452758  max resid 0.05175251 
## Run 8 stress 0.1208049 
## ... New best solution
## ... Procrustes: rmse 4.179053e-06  max resid 8.571987e-06 
## ... Similar to previous best
## Run 9 stress 0.1218747 
## Run 10 stress 0.1223134 
## Run 11 stress 0.1211578 
## ... Procrustes: rmse 0.01452878  max resid 0.05175696 
## Run 12 stress 0.120805 
## ... Procrustes: rmse 1.900015e-05  max resid 4.648789e-05 
## ... Similar to previous best
## Run 13 stress 0.1669989 
## Run 14 stress 0.1211578 
## ... Procrustes: rmse 0.01452832  max resid 0.05175863 
## Run 15 stress 0.1660973 
## Run 16 stress 0.1211578 
## ... Procrustes: rmse 0.01453113  max resid 0.0517506 
## Run 17 stress 0.1208049 
## ... Procrustes: rmse 9.373579e-07  max resid 2.849566e-06 
## ... Similar to previous best
## Run 18 stress 0.1211578 
## ... Procrustes: rmse 0.01452917  max resid 0.05175842 
## Run 19 stress 0.1211578 
## ... Procrustes: rmse 0.01452834  max resid 0.05175799 
## Run 20 stress 0.1220804 
## *** Solution reached
## Run 0 stress 0.0911341 
## Run 1 stress 0.08179215 
## ... New best solution
## ... Procrustes: rmse 0.0888207  max resid 0.3492943 
## Run 2 stress 0.09165344 
## Run 3 stress 0.09113433 
## Run 4 stress 0.09165304 
## Run 5 stress 0.09113439 
## Run 6 stress 0.08913187 
## Run 7 stress 0.09236561 
## Run 8 stress 0.08179264 
## ... Procrustes: rmse 0.0002713964  max resid 0.0009849656 
## ... Similar to previous best
## Run 9 stress 0.07961571 
## ... New best solution
## ... Procrustes: rmse 0.1153149  max resid 0.2288016 
## Run 10 stress 0.08915446 
## Run 11 stress 0.09113402 
## Run 12 stress 0.08179247 
## Run 13 stress 0.08023629 
## Run 14 stress 0.3841428 
## Run 15 stress 0.09731315 
## Run 16 stress 0.08179313 
## Run 17 stress 0.07961325 
## ... New best solution
## ... Procrustes: rmse 0.0007558797  max resid 0.001625623 
## ... Similar to previous best
## Run 18 stress 0.0817938 
## Run 19 stress 0.09152179 
## Run 20 stress 0.09113361 
## *** Solution reached
## INFO [2020-08-20 11:43:35] Done.
## INFO [2020-08-20 11:43:35] Saving ...

## INFO [2020-08-20 11:43:37] Supplement Beta plots ...
## INFO [2020-08-20 11:43:37] Done.
## INFO [2020-08-20 11:43:37] Global3...
## [1] ""
## INFO [2020-08-20 11:43:37] No glom ...
## INFO [2020-08-20 11:43:37] Bray ...
## 
##  mutant sauvage 
##      12      12 
## INFO [2020-08-20 11:43:37] Done
## INFO [2020-08-20 11:43:37] Unifrac ...
## INFO [2020-08-20 11:43:38] Done
## INFO [2020-08-20 11:43:38] wunifrac ...
## INFO [2020-08-20 11:43:38] Done
## 
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1    0.5075 0.50751  1.9574 0.06845 0.098901 .  
## souche     1    1.4622 1.46217  5.6393 0.19720 0.000999 ***
## Residuals 21    5.4449 0.25928         0.73435             
## Total     23    7.4146                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1  1.529137 5.715979 0.2062341   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)    
## Depth      1   0.14109 0.14109  1.3569 0.04814 0.238761    
## souche     1   0.60653 0.60653  5.8333 0.20693 0.000999 ***
## Residuals 21   2.18352 0.10398         0.74494             
## Total     23   2.93114                 1.00000             
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.6411514 6.159557 0.2187377   0.001      0.001  **
## 
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
## 
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1,      collapse = "+"), "+", col)), data = mdata, permutations = 1000) 
## 
## Permutation: free
## Number of permutations: 1000
## 
## Terms added sequentially (first to last)
## 
##           Df SumsOfSqs MeanSqs F.Model      R2   Pr(>F)   
## Depth      1   0.34096 0.34096  4.5057 0.14820 0.004995 **
## souche     1   0.37060 0.37060  4.8974 0.16108 0.006993 **
## Residuals 21   1.58915 0.07567         0.69072            
## Total     23   2.30071                 1.00000            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
##               pairs Df SumsOfSqs  F.Model        R2 p.value p.adjusted sig
## 1 sauvage vs mutant  1 0.4044203 4.691911 0.1757803   0.002      0.002   *
## INFO [2020-08-20 11:43:38] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764 
## Run 1 stress 0.1004766 
## ... Procrustes: rmse 0.0001305564  max resid 0.0003850547 
## ... Similar to previous best
## Run 2 stress 0.1004882 
## ... Procrustes: rmse 0.0058278  max resid 0.02233927 
## Run 3 stress 0.3224833 
## Run 4 stress 0.1275006 
## Run 5 stress 0.1004883 
## ... Procrustes: rmse 0.005792628  max resid 0.02227756 
## Run 6 stress 0.1004765 
## ... Procrustes: rmse 3.872382e-05  max resid 9.635155e-05 
## ... Similar to previous best
## Run 7 stress 0.1282056 
## Run 8 stress 0.1004882 
## ... Procrustes: rmse 0.005807573  max resid 0.02226059 
## Run 9 stress 0.1004764 
## ... Procrustes: rmse 5.338605e-05  max resid 0.0001382309 
## ... Similar to previous best
## Run 10 stress 0.1282049 
## Run 11 stress 0.1004882 
## ... Procrustes: rmse 0.005849562  max resid 0.02246848 
## Run 12 stress 0.1278163 
## Run 13 stress 0.1384184 
## Run 14 stress 0.1004882 
## ... Procrustes: rmse 0.005814917  max resid 0.02231782 
## Run 15 stress 0.1004764 
## ... Procrustes: rmse 1.65062e-05  max resid 4.742263e-05 
## ... Similar to previous best
## Run 16 stress 0.1004882 
## ... Procrustes: rmse 0.00581589  max resid 0.02233953 
## Run 17 stress 0.1004764 
## ... Procrustes: rmse 2.533215e-05  max resid 7.100506e-05 
## ... Similar to previous best
## Run 18 stress 0.1316327 
## Run 19 stress 0.1282053 
## Run 20 stress 0.1004882 
## ... Procrustes: rmse 0.005813854  max resid 0.02230593 
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882 
## Run 1 stress 0.3812595 
## Run 2 stress 0.2427133 
## Run 3 stress 0.1322745 
## Run 4 stress 0.1278118 
## Run 5 stress 0.1004882 
## ... Procrustes: rmse 2.675765e-05  max resid 7.361028e-05 
## ... Similar to previous best
## Run 6 stress 0.1322881 
## Run 7 stress 0.1004882 
## ... New best solution
## ... Procrustes: rmse 1.017588e-05  max resid 2.074624e-05 
## ... Similar to previous best
## Run 8 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 0.005816595  max resid 0.02230714 
## Run 9 stress 0.1004764 
## ... Procrustes: rmse 1.25171e-05  max resid 3.671702e-05 
## ... Similar to previous best
## Run 10 stress 0.1004884 
## ... Procrustes: rmse 0.005829559  max resid 0.02234914 
## Run 11 stress 0.1332142 
## Run 12 stress 0.1282046 
## Run 13 stress 0.3812845 
## Run 14 stress 0.1282056 
## Run 15 stress 0.1004884 
## ... Procrustes: rmse 0.005822553  max resid 0.02230221 
## Run 16 stress 0.1004764 
## ... New best solution
## ... Procrustes: rmse 1.551794e-05  max resid 4.493506e-05 
## ... Similar to previous best
## Run 17 stress 0.1004882 
## ... Procrustes: rmse 0.005814098  max resid 0.02231398 
## Run 18 stress 0.1004882 
## ... Procrustes: rmse 0.005824241  max resid 0.02234095 
## Run 19 stress 0.1004764 
## ... Procrustes: rmse 1.352306e-05  max resid 3.674424e-05 
## ... Similar to previous best
## Run 20 stress 0.1274956 
## *** Solution reached
## Run 0 stress 0.1224592 
## Run 1 stress 0.1235196 
## Run 2 stress 0.147283 
## Run 3 stress 0.1235347 
## Run 4 stress 0.169011 
## Run 5 stress 0.1228687 
## ... Procrustes: rmse 0.009360572  max resid 0.0342429 
## Run 6 stress 0.1224592 
## ... New best solution
## ... Procrustes: rmse 0.001457613  max resid 0.005158155 
## ... Similar to previous best
## Run 7 stress 0.1224592 
## ... Procrustes: rmse 0.001546598  max resid 0.005468143 
## ... Similar to previous best
## Run 8 stress 0.1224592 
## ... Procrustes: rmse 0.001544245  max resid 0.005459348 
## ... Similar to previous best
## Run 9 stress 0.1234827 
## Run 10 stress 0.1224592 
## ... Procrustes: rmse 7.37536e-05  max resid 0.0002022854 
## ... Similar to previous best
## Run 11 stress 0.1235046 
## Run 12 stress 0.1235044 
## Run 13 stress 0.1235043 
## Run 14 stress 0.1224029 
## ... New best solution
## ... Procrustes: rmse 0.01278869  max resid 0.04410855 
## Run 15 stress 0.1235043 
## Run 16 stress 0.1235195 
## Run 17 stress 0.1235043 
## Run 18 stress 0.1237818 
## Run 19 stress 0.1664051 
## Run 20 stress 0.1235043 
## *** No convergence -- monoMDS stopping criteria:
##     19: stress ratio > sratmax
##      1: scale factor of the gradient < sfgrmin
## Run 0 stress 0.09936821 
## Run 1 stress 0.09937007 
## ... Procrustes: rmse 0.001310072  max resid 0.003921009 
## ... Similar to previous best
## Run 2 stress 0.1012286 
## Run 3 stress 0.08369252 
## ... New best solution
## ... Procrustes: rmse 0.08719136  max resid 0.3717686 
## Run 4 stress 0.08804483 
## Run 5 stress 0.08804581 
## Run 6 stress 0.1031551 
## Run 7 stress 0.08369227 
## ... New best solution
## ... Procrustes: rmse 6.634335e-05  max resid 0.0002523386 
## ... Similar to previous best
## Run 8 stress 0.09856258 
## Run 9 stress 0.1031617 
## Run 10 stress 0.1031506 
## Run 11 stress 0.09856061 
## Run 12 stress 0.2121487 
## Run 13 stress 0.1031548 
## Run 14 stress 0.08804426 
## Run 15 stress 0.09856277 
## Run 16 stress 0.08804462 
## Run 17 stress 0.09856058 
## Run 18 stress 0.1174749 
## Run 19 stress 0.08483097 
## Run 20 stress 0.0848319 
## *** Solution reached
## INFO [2020-08-20 11:43:39] Done.
## INFO [2020-08-20 11:43:39] Saving ...

## INFO [2020-08-20 11:43:41] Supplement Beta plots ...
## INFO [2020-08-20 11:43:41] Done.
## INFO [2020-08-20 11:43:41] Finish